Molar trimming prediction and validation using machine learning

ABSTRACT

Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient&#39;s dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/741,465, filed Oct. 4, 2018, titled “AUTOMATIC MOLARTRIMMING PREDICTION AND VALIDATION USING MACHINE LEARNING,” which isherein incorporated by reference in its entirety.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare incorporated herein by reference in their entirety to the sameextent as if each individual publication or patent application wasspecifically and individually indicated to be incorporated by reference.

BACKGROUND

Orthodontic procedures may involve repositioning a patient's teeth to adesired arrangement in order to correct malocclusions and/or improveaesthetics. To achieve these objectives, orthodontic appliances such asbraces, orthodontic aligners, etc. can be applied to the patient's teethto effect desired tooth movements according to a treatment plan.

Orthodontic aligners may include devices that are removable and/orreplaceable over the teeth. Orthodontic aligners may be provided as partof an orthodontic treatment plan. In some orthodontic treatment plans, apatient may be provided plurality of orthodontic aligners over thecourse of treatment to make incremental position adjustments to thepatient's teeth.

Many orthodontic treatment plans include a processing workflow that caninclude performing a 3D scan of the teeth, segmenting the 3D scan intoindividual teeth, determining an orthodontic treatment plan, and sendingthe case to a doctor for review. In some situations, the 3D scan itselfcontains problems in the terminal molar area. For example, a molar caninclude portions that are not scanned (i.e., missing data), can bepartially erupted, or can have gingiva covering a portion of the molar.

SUMMARY OF THE DISCLOSURE

Implementations address the need to provide an automated tooth trimmingand segmentation system to effectively and accurately identify missingor incomplete data in 3D tooth models and trim or remove portions of the3D tooth model corresponding to the missing data. Examples of missing orincomplete data include not-scanned areas, partially erupted teeth, orgingiva covering a portion of the tooth. The present applicationaddresses these and other technical problems by providing technicalsolutions and/or automated agents that segment and trim 3D tooth scandata in dental patients without human intervention. Tooth trimming andsegmentation may provide the basis for implementation of automatedorthodontic treatment plans, design and/or manufacture of orthodonticaligners (including series of polymeric orthodontic aligners thatprovide forces to correct malocclusions in patients' teeth). Theseapparatuses and/or methods may provide or modify a treatment plan,including an orthodontic treatment plan. The apparatuses and/or methodsdescribed herein may provide instructions to generate and/or maygenerate a set or series of aligners, and/or orthodontic treatment plansusing orthodontic aligners that incorporate the automated tooth trimmingand segmentation. The apparatuses and/or methods described herein mayprovide a visual representation of the patient's teeth.

In general, example apparatuses (e.g., devices, systems, etc.) and/ormethods described herein may acquire a representation of a patient'steeth including tooth characteristics for use as the raw features in thescoring model. The raw features may be extracted from a 3D model of thepatient's teeth (e.g., a 3D tooth point cloud). In some implementations,a subset of the 3D tooth point cloud (e.g., a specific number of pointsrepresenting each tooth) can be used as the raw features.

In general, example apparatuses (e.g., devices, systems, etc.) and/ormethods described herein may include an automated process fordetermining if trimming of the molars is desired and for carrying outthe trimming process. In some examples, the apparatuses and/or methodscan implement machine learning classification models to determine iftrimming is desired and to carry out the trimming process. Examples ofmachine learning systems that may be used include, but are not limitedto, Convolutional Neural Networks (CNN), Decision Tree, Random Forest,Logistic Regression, Support Vector Machine, AdaBoosT, K-NearestNeighbor (KNN), Quadratic Discriminant Analysis, Neural Network, etc.The machine learning classification models can be configured to generatean output data set that includes a probability that the data set needsto be trimmed and a location where the data set needs to be trimmed. Insome examples, the machine learning classification model can output alinear scale rating (e.g., a probability between 0.0 to 1.0).

A “patient,” as used herein, may be any subject (e.g., human, non-human,adult, child, etc.) and may be alternatively and equivalently referredto herein as a “patient” or a “subject.” A “patient,” as used herein,may but need not be a medical patient. A “patient,” as used herein, mayinclude a person who receives orthodontic treatment, includingorthodontic treatment with a series of orthodontic aligners.

Any of the apparatuses and/or methods described herein may be part of adistal tooth scanning apparatus or method, or may be configured to workwith a digital scanning apparatus or method.

As will be described in greater detail herein, apparatuses and/ormethods described herein (e.g., for each of a patient's teeth) mayinclude collecting a 3D scan of the patient's teeth. Collecting the 3Dscan may include taking the 3D scan, including scanning the patient'sdental arch directly (e.g., using an intraoral scanner) or indirectly(e.g., scanning an impression of the patient's teeth), acquiring the 3Dscan information from a separate device and/or third party, acquiringthe 3D scan from a memory, or the like. The 3D scan can generate a 3Dmesh of points representing the patient's arch, including the patient'steeth and gums.

Additional information may be collected with the 3D scan, includingpatient information (e.g., age, gender, etc.).

The 3D scan information may be standardized and/or normalized.Standardizing the scan may include converting the 3D scan into astandard format (e.g., a tooth surface mesh), and/or expressing the 3Dscan as a number of angles (e.g., vector angles) from a center point ofeach tooth, etc. In some variations, standardizing may includenormalizing the 3D scan using another tooth, including stored toothvalues.

Standardizing may include identifying a predetermined number of anglesrelative to a center point of the target tooth. Any appropriate methodmay be used to determine the center of the tooth. For example, thecenter of the tooth may be determined from a mesh point representationof each tooth (e.g., from a segmented version of the 3D scanrepresenting a digital model of the patient's teeth) by determining thegeometric center of the mesh points for each tooth, by determining thecenter of gravity of the segmented tooth, etc. The same method fordetermining the center of each tooth may be consistently applied betweenthe teeth and any teeth used to form (e.g., train) the systems describedherein.

Standardizing may be distinct from normalizing. As used herein,standardizing may involve regularizing numerical and/or otherdescription(s) of a tooth. For example, standardizing may involveregularizing the order and/or number of angles (from the center of thetooth) used to describe the teeth. The sizes of the teeth from theoriginal 3D scan may be maintained.

Any appropriate features may be extracted from the prepared (e.g.,standardized and/or normalized) teeth. For example, in some variations,features may include a principal component analysis (PCA) for each ofthe teeth in the dental arch being examined. Additional features (e.g.,geometric descriptions of the patient's teeth) may not be desired (e.g.,PCA alone may be used) or it may be used to supplement the PCA of eachtooth. PCA may be performed on the standardized teeth using anyappropriate technique, as discussed above, including using modules fromexisting software environments such C++ and C # (e.g., ALGLIB librarythat implements PCA and truncated PCA, MLPACK), Java (e.g., KNIME, Weka,etc.), Mathematica, MATLAB (e.g., MATLAB Statistics Toolbox, etc.),python (e.g., numpy, Scikit-learn, etc.), GNU Octave, etc.

In some examples, the apparatuses and/or methods herein may segment apatient's teeth from the 3D scan information without human intervention,and this segmentation information may be used to simulate, modify and/orchoose between various orthodontic treatment plans. For example,segmentation can be performed by a computing system by evaluating data(such as three-dimensional scan, or a dental impression) of thepatient's teeth or arch to separate the 3D mesh of points intoindividual teeth and gums.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe claims that follow. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1A is a diagram showing an example of a computing environmentconfigured to digitally scan a dental arch, identify missing data in 3Dtooth scans, and trim or remove portions of the 3D tooth modelcorresponding to the missing data.

FIG. 1B is a diagram showing an example of segmentation engine(s).

FIG. 1C is a diagram showing an example of a feature extractionengine(s).

FIG. 1D is a diagram showing an example of a trimming engine(s).

FIGS. 2A-2F illustrate one example of an input for a trimming enginecomprising a 3D model of the patient's teeth (showing six views).

FIG. 3 shows one example of an output of a machine learning engine asdescribed herein.

FIGS. 4A-4B illustrate building a parametric model to identify a trimplane.

FIGS. 4C-4E illustrate adjusting the trim plane by tooth axes.

FIGS. 5A-5E illustrate examples of a tooth (e.g., a molar) that requirestrimming as identified by a machine learning engine described herein.

FIG. 6 is a flowchart describing one example of determining apost-treatment tooth position score.

FIG. 7 is another flowchart describing one example of determining apost-treatment tooth position score.

FIG. 8 is a simplified block diagram of a data processing system thatmay perform the methods described herein.

DETAILED DESCRIPTION

Described herein are apparatuses (e.g., systems, computing devicereadable media, devices, etc.) and methods for accurately identifyingmissing or incomplete data in 3D tooth models and removing portions ofthe 3D tooth model corresponding to the missing or incomplete data. Oneobject of the present disclosure is to use machine learning technologyto provide a classifier that can determine if a 3D tooth modelrepresenting one or more of the patient's teeth needs to be trimmed andto trim and/or remove the data from the 3D tooth model corresponding tothe tooth that needs to be trimmed. A “classifier,” as used herein, mayincorporate one or more automated agents to predict one or more classesof given data points. A classifier can include machine learningtechniques, as discussed further herein. The word “trimming,” (andvariants “trim,” “trimmed,” etc.) as used herein, may includecomputer-implemented operations to remove at least a part of a 3D toothmodel. Examples of trimming operations include removing part ofrepresentations of molars, bicuspids, canines, incisors, etc., from a 3Dtooth model. Trimming can be used in conjunction with modeling treatmentplans. For some treatment plans, it may be desirable to remove parts ofa molar (e.g., molars with portions that are not scanned (i.e., missingdata), molars that are partially erupted, and/or molars having gingivacovering a portion thereof) from a 3D tooth model. The classifier canmake such determinations based upon various data including patientdemographics, tooth measurements, tooth surface mesh, processed toothfeatures, and historical patient data. These methods and apparatus canuse this information to provide an output that indicates a probabilitythat trimming is desirable and/or location(s) to be trimmed. Suchdeterminations may form the basis of treatment planning by creating 3Dtooth models that are useful for treatment planning and/or fabricationof orthodontic appliances implementing such treatment plans.

For example, described herein are apparatuses and/or methods, e.g.,systems, including systems to implement processes that incorporate atooth trimming system without human intervention. When the system istriggered by a request for a trimming assessment, the system canretrieve relevant tooth/patient information from a local or remotedatabase, process the information, and convert the information intorepresentative features. The features can be passed into a trimmingclassification model, which may use machine learning technology (e.g.,Convolutional Neural Network (CNN), Decision Tree, Random Forest,Logistic Regression, Support Vector Machine, AdaBOOST, K-NearestNeighbor (KNN), Quadratic Discriminant Analysis, Neural Network, etc.)to return a probability that 3D tooth model representing the patient'steeth needs to be trimmed, and the location to be trimmed. Theparameters inputted into the tooth scoring classification model can beoptimized with historic data. The tooth trimming classification modelmay be used to provide an output indicating the probability that 3Dtooth model of the patient's teeth requires trimming and the location ofthe tooth or teeth that need to be trimmed. The results may be providedon demand and/or may be stored in a memory (e.g., database) for lateruse.

The apparatuses and/or methods described herein may be useful inplanning and fabrication of dental appliances, including elasticpolymeric positioning appliances, is described in detail in U.S. Pat.Nos. 5,975,893, 6,409,504, and in published PCT application WO 98/58596,which is herein incorporated by reference for all purposes. Systems ofdental appliances employing technology described in U.S. Pat. No.5,975,893 are commercially available from Align Technology, Inc., SantaClara, Calif., under the tradename, Invisalign System.

Throughout the body of the Description of Embodiments, the use of theterms “orthodontic aligner”, “aligner”, or “dental aligner” issynonymous with the use of the terms “appliance” and “dental appliance”in terms of dental applications. For purposes of clarity, embodimentsare hereinafter described within the context of the use and applicationof appliances, and more specifically “dental appliances.”

FIG. 1A is a diagram showing an example of a computing environment 100Aconfigured to facilitate gathering digital scans of a dental arch withteeth therein. The environment 100A includes a computer-readable medium152, a scanning system 154, a dentition display system 156, and a toothtrimming system 158. One or more of the modules in the computingenvironment 100A may be coupled to one another or to modules notexplicitly shown.

The computer-readable medium 152 and other computer readable mediadiscussed herein are intended to represent a variety of potentiallyapplicable technologies. For example, the computer-readable medium 152can be used to form a network or part of a network. Where two componentsare co-located on a device, the computer-readable medium 152 can includea bus or other data conduit or plane. Where a first component isco-located on one device and a second component is located on adifferent device, the computer-readable medium 152 can include awireless or wired back-end network or LAN. The computer-readable medium152 can also encompass a relevant portion of a WAN or other network, ifapplicable.

The scanning system 154 may include a computer system configured to scana patient's dental arch. A “dental arch,” as used herein, may include atleast a portion of a patient's dentition formed by the patient'smaxillary and/or mandibular teeth, when viewed from an occlusalperspective. A dental arch may include one or more maxillary ormandibular teeth of a patient, such as all teeth on the maxilla ormandible or a patient. The scanning system 154 may include memory, oneor more processors, and/or sensors to detect contours on a patient'sdental arch. The scanning system 154 may be implemented as a camera, anintraoral scanner, an x-ray device, an infrared device, etc. Thescanning system 154 may include a system configured to provide a virtualrepresentation of a physical mold of patient's dental arch. The scanningsystem 154 may be used as part of an orthodontic treatment plan. In someimplementations, the scanning system 154 is configured to capture apatient's dental arch at a beginning stage, an intermediate stage, etc.of an orthodontic treatment plan.

The dentition display system 156 may include a computer systemconfigured to display at least a portion of a dentition of a patient.The dentition display system 154 may include memory, one or moreprocessors, and a display device to display the patient's dentition. Thedentition display system 156 may be implemented as part of a computersystem, a display of a dedicated intraoral scanner, etc. In someimplementations, the dentition display system 156 facilitates display ofa patient's dentition using scans that are taken at an earlier dateand/or at a remote location. It is noted the dentition display system156 may facilitate display of scans taken contemporaneously and/orlocally to it as well. As noted herein, the dentition display system 156may be configured to display the intended or actual results of anorthodontic treatment plan applied to a dental arch scanned by thescanning system 154. The results may include 3D virtual representationsof the dental arch, 2D images or renditions of the dental arch, etc.

The tooth trimming system 158 may include a computer system configuredto process 3D scans or meshes of a patient's dentition taken by thescanning system 154. As noted herein, the tooth trimming system 158 maybe configured to determine a probability that the 3D tooth model of thepatient's dentition requires trimming, and may also be configured totrim the 3D tooth model. The tooth trimming system 158 may includesegmentation engine(s) 160, feature extraction engine(s) 162, andtrimming engine(s) 164. One or more of the modules of the image trimmingsystem 158 may be coupled to each other or to modules not shown.

As used herein, any “engine” may include one or more processors or aportion thereof. A portion of one or more processors can include someportion of hardware less than all of the hardware comprising any givenone or more processors, such as a subset of registers, the portion ofthe processor dedicated to one or more threads of a multi-threadedprocessor, a time slice during which the processor is wholly orpartially dedicated to carrying out part of the engine's functionality,or the like. As such, a first engine and a second engine can have one ormore dedicated processors or a first engine and a second engine canshare one or more processors with one another or other engines.Depending upon implementation-specific or other considerations, anengine can be centralized or its functionality distributed. An enginecan include hardware, firmware, or software embodied in acomputer-readable medium for execution by the processor. The processortransforms data into new data using implemented data structures andmethods, such as is described with reference to the figures herein.

The engines described herein, or the engines through which the systemsand devices described herein can be implemented, can be cloud-basedengines. As used herein, a cloud-based engine is an engine that can runapplications and/or functionalities using a cloud-based computingsystem. All or portions of the applications and/or functionalities canbe distributed across multiple computing devices, and need not berestricted to only one computing device. In some embodiments, thecloud-based engines can execute functionalities and/or modules that endusers access through a web browser or container application withouthaving the functionalities and/or modules installed locally on theend-users' computing devices.

As used herein, “datastores” may include repositories having anyapplicable organization of data, including tables, comma-separatedvalues (CSV) files, traditional databases (e.g., SQL), or otherapplicable known or convenient organizational formats. Datastores can beimplemented, for example, as software embodied in a physicalcomputer-readable medium on a specific-purpose machine, in firmware, inhardware, in a combination thereof, or in an applicable known orconvenient device or system. Datastore-associated components, such asdatabase interfaces, can be considered “part of” a datastore, part ofsome other system component, or a combination thereof, though thephysical location and other characteristics of datastore-associatedcomponents is not critical for an understanding of the techniquesdescribed herein.

Datastores can include data structures. As used herein, a data structureis associated with a particular way of storing and organizing data in acomputer so that it can be used efficiently within a given context. Datastructures are generally based on the ability of a computer to fetch andstore data at any place in its memory, specified by an address, a bitstring that can be itself stored in memory and manipulated by theprogram. Thus, some data structures are based on computing the addressesof data items with arithmetic operations; while other data structuresare based on storing addresses of data items within the structureitself. Many data structures use both principles, sometimes combined innon-trivial ways. The implementation of a data structure usually entailswriting a set of procedures that create and manipulate instances of thatstructure. The datastores, described herein, can be cloud-baseddatastores. A cloud-based datastore is a datastore that is compatiblewith cloud-based computing systems and engines.

The segmentation engine(s) 160 may be configured to implement one ormore automated agents configured to process tooth scans from thescanning system 154. The segmentation engine(s) 160 may include graphicsengines to process images or scans of a dental arch. In someimplementations, the segmentation engine(s) 160 format scan data from anscan of a dental arch into a dental mesh model (e.g., a 3D tooth model)of the dental arch. In other embodiments, the segmentation engine(s) 160can format 2D or 3D images of the dental arch into a dental mesh model.For example, multiple 2D images of the patient's teeth can be input intothe segmentation engine(s) 160 to form the dental mesh model. The 2Dimages can comprise, for example, multiple images of the patient'steeth. In some embodiments, the patient's dental arch is divided intoquarters, and multiple input 2D images for each quarter of the patient'sdental arch can be used to generate the dental mesh model. Thesegmentation engine(s) 160 may also be configured to segment the 3Ddental mesh model of the dental arch into individual dental components,including segmenting the 3D tooth model into 3D tooth models ofindividual teeth. The 3D tooth models of the dental arch and/or theindividual teeth may comprise geometric point clouds or polyhedralobjects that depict teeth and/or other elements of the dental arch in aformat that can be rendered on the dentition display system 156. In someimplementations, the segmentation engine(s) 160 may determine the centerof one or more individual teeth of the 3D tooth model. The center of thetooth may be determined from a mesh point representation of each tooth(e.g., from a segmented version of the 3D scan representing a digitalmodel of the patient's teeth) by determining the geometric center of themesh points for each tooth, by determining the center of gravity of thesegmented tooth, etc. The segmentation engine(s) 160 may provide 3Dtooth models and/or other data, such as individual teeth centers, toother modules of the tooth trimming system 158.

The feature extraction engine(s) 162 may implement one or more automatedagents configured to extract dental features. A “dental feature,” asused herein, may include data points from the 3D dental mesh model thatcorrelate to geometrical properties (e.g., edges, contours, vertices,vectors, surfaces, etc.) of the patient's teeth. A “dental feature” maybe based on patient demographics and/or tooth measurements. A dentalfeature may be related to “PCA features,” e.g., those dental featuresderived from a principal component analysis (PCA) of a tooth. In someimplementations, the feature extraction engine(s) 162 is configured toanalyze 3D dental mesh models from the segmentation engine(s) 160 toextract the dental features. In one implementation, the featureextraction engine(s) 162 may, for each tooth under consideration,extract a subset of dental features from the 3D tooth model. Forexample, a specified number of tooth measurement points (e.g., ninetooth measurement points) can be extracted. This subset of measurementpoints can be selected to define the position and orientation of eachtooth, as well as partial information on the tooth shape.

The trimming engine(s) 164 may implement one or more automated agentsconfigured to predict a probability that the 3D tooth model of apatient's teeth (or portion thereof) relate to one or more trimmingfactors. “Trimming factors,” as used herein, may include any factorsthat form the basis of a trimming determination, e.g., a determinationwhether or not to trim a part of a 3D tooth model and/or the parts of a3D model where trimming is desirable. In some implementations, thetrimming engine(s) 164 may determine whether trimming is desired and/ormay also identify location(s), specific tooth, and/or specific teethwithin the 3D tooth model for which trimming would be desirable. In someimplementations, the trimming engine(s) 164 assign physical and/orgeometrical properties to a 3D dental mesh model that are related tophysical/geometrical properties of dental arches or teeth. The trimmingengine(s) 164 may acquire dental features from the feature extractionengine(s) 162 and apply machine learning algorithms to predict if itwould be desirable to trim the 3D tooth model representing the patient'steeth; it may also predict the location, tooth, or teeth for whichtrimming would be desirable. In some implementations, the trimmingengine(s) 164 use a trained convolutional neural network and/or trainedclassifiers to identify a probability that trimming would be desirablefor one or more teeth on a 3D tooth model. Examples of machine learningsystems implemented by the trimming engine(s) 164 may include DecisionTree, Random Forest, Logistic Regression, Support Vector Machine,AdaBOOST, K-Nearest Neighbor (KNN), Quadratic Discriminant Analysis,Neural Network, etc., to perform the trimming assessment.

If trimming is desired, the trimming engine(s) 164 may further implementone or more automated agents configured to identify an orientation andposition of a trim plane. A “trim plane,” as used herein, may include aplane that passes through a 3D virtual model and forms the basis of atrimming determination. Proper placement of the trim plane ensures thatany problem areas in the scan or 3D dental mesh model are removed, butenough of the scan or 3D dental mesh model remains to allow forconstruction or manufacturing of a dental aligner. In someimplementations, the trimming engine(s) may use the segmented model ofthe patient's teeth to build a parametric model representing an archportion corresponding to the patient's teeth. The parametric model cancomprise, for example, a quadratic Bezier curve. In someimplementations, the trimming engine(s) can find a trim plane normalvector on at least one of the teeth as a tangent to the parametricmodel. For example, the trimming engine(s) can find a trim plane normalvector at a corresponding last (or distal-most) molar center of theparametric model. In some implementations, the trim plane normal vectorcan be further adjusted by tooth axes. The trimming engine can trim theappropriate location, tooth, or teeth, and the trimmed location, tooth,or teeth can be incorporated into a final segmentation result.

The optional treatment modeling engine(s) 166 may be configured to storeand/or provide instructions to implement orthodontic treatment plansand/or the results of orthodontic treatment plans. The optionaltreatment modeling engine(s) 166 may provide the results of orthodontictreatment plans on a 3D dental mesh model. The optional treatmentmodeling engine(s) 166 may model the results of application oforthodontic aligners to the patient's dental arch over the course of anorthodontic treatment plan.

FIG. 1B is a diagram showing an example of the segmentation engine(s)160 a. The segmentation engine(s) 160 a may include an arch scanningengine 168 and an individual tooth segmentation datastore 170. One ormore of the modules of the segmentation engine(s) 160 a may be coupledto each other or to modules not shown.

The arch scanning engine 168 may implement one or more automated agentsconfigured to scan a scan of the patient's teeth, 2D or 3D images of thepatient's teeth, or a 3D dental mesh model for individual toothsegmentation data. “Individual tooth segmentation data,” as used herein,may include positions, geometrical properties (contours, etc.), toothcenters, and/or other data that can form the basis of segmentingindividual teeth from 3D dental mesh models of a patient's dental arch.The arch scanning engine 168 may implement automated agents to separatedental mesh data for individual teeth from a 3D dental mesh model of thedental arch. The arch scanning engine 168 may implement automated agentsto determine the center of individual teeth from a mesh pointrepresentation of each tooth by determining the geometric center of themesh points for each tooth, by determining the center of gravity of thesegmented tooth, etc. The arch scanning engine 168 may further implementautomated agents to number the individual teeth.

In embodiments where the inputs to the arch scanning engine 168 comprise2D or 3D images of the patient's teeth, the images can comprise, forexample, multiple images of the patient's teeth. In some embodiments,the patient's dental arch is divided into quarters, and multiple input2D images for each quarter of the patient's dental arch can be used togenerate the dental mesh model and/or to scan for individual toothsegmentation data. In one embodiment, the resolution of the images canbe 256×256 for each view (there can be multiple views per each quarter,such as four views per quarter). Each quarter of the dental arch caninclude its own machine learning network to scan for segmentation data.For each view, a machine learning network with a plurality of layers canbe used as an encoder with some additional “fully connected” layers tofuse activation from each views. In a specific embodiment, with aresolution of 256×256, the size of weights is 429 Mb for each network.Potentially these weights can be compressed by sharing parametersbetween branches. Thus, in one embodiment the system requires at least750 Mb in RAM to predict trimming for a given quarter.

The individual tooth segmentation datastore 170 may be configured tostore data related to model dental arches, including model dental archesthat have been segmented into individual teeth. The model dental archdata may comprise data related to segmented individual teeth, includingindividual tooth centers, and tooth identifiers of the individual teethsuch as tooth types and tooth numbers.

FIG. 1C is a diagram showing an example of a feature extractionengine(s) 162 a. The feature extraction engine(s) 162 a may include amesh extraction engine 172 and a tooth feature datastore 174. One ormore of the modules of the feature extraction engine(s) 162 a may becoupled to each other or to modules not shown.

The mesh extraction engine 172 may implement one or more automatedagents configured to determine or extract raw features from theindividual tooth segmentation data. The tooth shape features maycomprise, for example, the 3D point cloud, or alternatively, a subset ofdata points from the 3D point cloud specifically chosen to represent theshape, position, and orientation of the tooth.

The mesh extraction engine 172 may also implement one or more automatedagents configured to produce features for the scoring model. In oneexample, principal component analysis (PCA) can be implemented to obtainthe dental features that will be used by the scoring model. In oneimplementation, the 3D dental mesh model of individual teeth comprises ascatter plot of points representing a patient's tooth, and PCA can beapplied to the scatter plot to obtain vectors along the biggestdistribution of scatter plots. In another example, the mesh extractionengine 172 may implement automated feature exploration (e.g., using deepneural networks or other feature selection methods) to produce thefeatures for the scoring model.

The tooth feature datastore 176 may be configured to store data relatedto raw features or produced features from the modules described above.

FIG. 1D is a diagram showing an example of the trimming engine(s) 164 a.The trimming engine(s) 164 a may acquire raw and/or produced featuredata from the feature extraction engine(s) 162 a described above. Thetrimming engine(s) 164 a may include a machine learning engine 175, atrim plane engine 176, a scan trimming engine 177, and a trimmingdatastore 178. FIGS. 2A-2F illustrate one example of an input for thetrimming engine(s) 164 a, comprising a 3D model of the patient's teeth(shown from various views) from the arch scanning engine includingfeatures from the mesh extraction engine. For example, the 3D toothmodel in FIGS. 2A-2F may include individually segmented teeth thatprovide representations of the shape of each of the patient's teeth. Insome examples, the input can include depth maps of the teeth.

The machine learning engine 175 may implement one or more automatedagents configured to use machine learning techniques to classify aprobability that 3D tooth model of a patient's teeth requires trimmingand to identify the location to be trimmed. In some implementations, themachine learning engine 175 may acquire raw features and/or producedfeatures data from the feature extraction engine(s) 162 a. Using atrained classifier, the machine learning engine 175 may provide anidentifier (e.g., a statistical or other score) that determines aprobability that the 3D scan needs to be trimmed. The machine learningengine 175 may further provide a location within the 3D tooth model thatrequires trimming (e.g., identifying the individual tooth that requirestrimming).

As examples, the machine learning engine 175 may use a classifiertrained to correlate various dental features with whether the 3D toothmodel requires trimming. More specifically, the classifier may betrained to compare the geometry of a target tooth (e.g., an individuallysegmented molar from the 3D tooth model) to the ideal or general shapeassociated with that target tooth. The classifier can then return ascore that assesses how accurately the target tooth tracks the ideal orgeneral tooth shape. The classifier can further identify the locationwithin the 3D tooth model, or the individual tooth segmentation, thatrequires trimming. The machine learning engine 175 may incorporate oneor more machine learning techniques. Examples of such techniques includeConvolutional Neural Networks (CNN), Decision Tree, Random Forest,Logistic Regression, Support Vector Machine, AdaBOOST, K-NearestNeighbor (KNN), Quadratic Discriminant Analysis, Neural Network, etc.The machine learning engine 175 can provide an output with a probabilitythat the 3D tooth model of the patient's teeth requires trimming and thelocation of the tooth or teeth that requires trimming. The output canbe, for example, a linear score or a percentage (e.g., 0.0 to 1.0, 0 to10, 0% to 100%), a categorical output (e.g., “No Trim”, “Most Likely NoTrim”, “Likely Not Trim”, “Likely Trim”, “Most Likely Trim”, “Trim”,etc.), and optionally, a graphical representation of a 3D model of theteeth identifying the location that needs trimming.

When the machine learning engine has determined that the 3D tooth modelof the patient's teeth requires trimming with a sufficiently highprobability, the system can optionally output a graphical representationof the location of the 3D tooth model that requires trimming. FIG. 3 isan example of an optional graphical output of the machine learningengine 175, which identifies on the 3D tooth model the location, tooth,or teeth in the 3D tooth model that requires trimming. In this example,the machine learning engine 175 indicates the need for trimming in thelocation identified by region 180. Additionally, the graphical outputcan further include a numerical or written probability that theidentified location requires trimming (e.g., a percentage or linearoutput as described above).

The trim plane engine 176 may implement one or more automated agentsconfigured to identify a position and/or orientation of a trim plane ortrim planes within the 3D tooth model. In some implementations, thetrimming engine(s) may use the segmented model of the patient's teeth tobuild a parametric model representing an arch portion corresponding tothe patient's teeth. The parametric model can comprise, for example, aquadratic Bezier curve, however it should be understood that otherspecific parametric models can be used.

Referring to FIGS. 4A-4B, the trim plane engine can use a plurality oftooth features to build the parametric model. Generally, at least threetooth features are needed to build the parametric model, includingpreferably a tooth feature from a distal tooth on the left side of ajaw, a tooth feature from a mesial and/or centrally located tooth (e.g.,incisors or canines), and a tooth feature from a distal tooth on theright side of the jaw. In FIG. 4A, for example, at least tooth centerpoints 184 a and 184 b of the two distal-most molars (e.g., on the leftand right side, respectively) and an incisor center point 184 c can beused to build the parametric model. While tooth centers are the toothfeatures used to build the parametric model in the illustrated example,it should be understood that other tooth features can be used to buildthe model, including but not limited to lingual, buccal, or occlusalsurfaces, gaps or spaces between teeth, or specific structures on oraround the patient's teeth. Still referring to FIG. 4A, straight linesconnecting adjacent tooth features provides an initial representation ofthe parametric model. FIG. 4B is an illustration in which the parametricmodel has been applied to the input tooth features to provide a smoothcurve representative of the patient's dental arch associated with thoseinput tooth features.

The trim plane engine 176 may further implement one or more automatedagents to find a trim plane normal vector as a tangent to the parametricmodel. In some implementations, the trim plane normal vector is thetangent line to the curve of the parametric model at the tooth featureof the distal-most, or last input tooth. For example, referring to FIG.4B, the trim plane engine can be configured to find the tangent line tothe parametric model at the tooth center point 184 b of the distal-mostmolar on the right side of the jaw. In the example where the parametricmodel comprises a Bezier curve, the trim plane engine can be configuredto find the tangent line to the Bezier curve at either parameter 0.0 or1.0 on the Bezier curve.

Once the trim plane engine has found the trim plane normal vector, asdescribed above, the trim plane engine can further implement one or moreautomated agents to adjust and fine tune the orientation of the trimplane normal vector with adjustments along tooth axes, including alongthe x-axis (e.g., moving to the buccal side of the tooth), along they-axis (e.g., moving in the mesial direction for teeth on the right sideof the jaw or moving in the distal direction for teeth on the left sideof the jaw), and along the z-axis (e.g., going up from the lower jaw ordown from the upper jaw). FIG. 4C is an illustration showing the toothaxes for both the distal-most molars in a patient's jaw.

Referring to FIG. 4D, the trim plane normal vector can be adjusted byrotating the normal vector around the tooth z-axis in the direction ofthe tooth y-axis. In some embodiments, the rotation angle is the minimumof either half the angle α between the normal vector and the toothy-axis or a pre-determined rotation angle. In one example, thepredetermined rotation angle can be 7.5 degrees. FIG. 4D is anillustration showing an adjusted orientation of the trim plane normalvector in the direction of the tooth y-axis.

The trim plane engine can further implement one or more automated agentsto adjust the trim plane normal vector angulation. In one embodiment,referring to FIG. 4E, the trim plane engine can find the angle α betweenthe tooth z-axis and the direction from the tooth center to the adjacentmesial tooth center. If this angle is less than π/2, the trim planeengine can be configured to rotate the normal vector by π/2−α.

The scan trimming engine 177 may implement one or more automated agentsconfigured to modify the 3D tooth model by trimming the tooth or teethidentified by the machine learning engine 175. For example, the 3D toothmodel may be trimmed along the trim plane(s) identified by trim planeengine 176. In some implementations, only a portion of the tooth orteeth that requires trimming is trimmed by the scan trimming engine. Forexample, only the portion of the tooth or teeth that have missing orincomplete data may be trimmed or removed from the 3D tooth model (e.g.,the portion of a tooth that is unerupted or covered in gingiva). Thismay be, for example, approximately ⅓ of the target tooth that istrimmed, ½ of the target tooth that is trimmed, ⅔ of the target tooththat is trimmed, etc.

FIGS. 5A-5E illustrate examples of a tooth (e.g., a molar) that requirestrimming as identified by the machine learning engine 175. For example,the machine learning engine 175 may have determined that the tooth ispartially covered in gingiva, is partially erupted, has missing scandata, or has distortions. The cutting line 182 for trimming ispositioned along the trim plane as determined by the trim plane engine.

The trimming datastore 178 may be configured to store data relating tothe probability that the 3D tooth model requires trimming from themachine learning engine 175 (e.g., the output from the machine learningengine) and the trim plane identified by trim plane engine 176. In someimplementations, the trimming probability is a linear labeling score(e.g., a score ranging from 0.0 to 1.0, where 0.0 indicates a lowprobability that the 3D tooth model requires trimming and 1.0 indicatesa high probability that the 3D tooth model requires trimming).Alternatively, the trimming probability can be a categorical output(e.g., “No Trim”, “Most Likely No Trim”, “Likely Not Trim”, “LikelyTrim”, “Most Likely Trim”, “Trim”, etc.). The trimming datastore 178 maybe further configured to store the location of the tooth or teeth thatrequires trimming. Additionally, the trimming datastore 178 may beconfigured to store the modified 3D tooth model or 3D model aftertrimming has been performed by the scan trimming engine 177.

FIG. 6 illustrates one example of a method for trimming missing orincomplete data from a 3D tooth model of a patient's dental arch. Thismethod may be implemented by a system, such as one or more of thesystems in the computing environment 100A, shown in FIG. 1A. At anoperation 602, the system may collect a three-dimensional (3D) scan ofthe patient's dental arch. The 3D scan may be collected directly fromthe patient (e.g., using an intraoral scanner) or indirectly (e.g., byscanning a mold of the patient's dentition and/or be receiving a digitalmodel of the patient taken by another, etc.).

The 3D scan may be prepared for further processing. For example, the 3Dscan may be expressed as a digital mesh and/or segmented into individualteeth (and non-teeth elements, such as gingiva, arch, etc.). At anoperation 604, raw dental features may be extracted from the 3D model ofthe patient's teeth (and in some variations from additional data aboutthe patient or the patient's teeth or from prescription guidelines),e.g., using a feature extraction engine. For example, features from theindividual tooth segmentation data may be extracted with the featureextraction engine. The tooth shape features may comprise, for example,the 3D point cloud, or alternatively, a subset of data points from the3D point cloud specifically chosen to represent the shape, position, andorientation of the tooth.

At an optional operation 606, additional features for the scoring modelmay be produced. In one example, principal component analysis (PCA) canbe implemented to obtain the dental features that will be used by thescoring model. In one implementation, the 3D dental mesh model ofindividual teeth comprises a scatter plot of points representing apatient's tooth, and PCA can be applied to the scatter plot to obtainvectors along the biggest distribution of scatter plots. In anotherexample, automated feature exploration can be implemented (e.g., usingdeep neural networks or other feature selection methods) to produce thefeatures for the scoring model.

At an operation 608, the extracted dental features and the producedfeatures may be used exclusively or in combination with any otherextracted feature described herein. The dental features may be providedto the classifier to determine the probability that the 3D tooth modelrequires trimming and to identify the location, tooth, or teeth withinthe 3D tooth model that requires trimming.

At an operation 610, the probability that the 3D tooth model requirestrimming and the location, tooth, or teeth within the 3D tooth modelthat requires trimming may then be output. In some variations thisinformation is used to modify a model (e.g., a 3D digital model) of thepatient's teeth (e.g., dental arch). For example, at an optionaloperation 612, a trim plane can be identified when trimming isdesirable. The trim plane may be identified from a parametric modelrepresenting an arch portion corresponding to the patient's teeth, asdescribed above. In some implementations, the trim plane comprises anormal vector on at least one of the teeth as a tangent to theparametric model. The 3D tooth model may be trimmed at the trim plan toremove the missing or incomplete data identified by the machine learningalgorithm.

FIG. 7 is another flowchart that shows the overall trimming systemdescribed above and describes a method of extracting and generating datato be passed into a tooth trimming system.

At an operation 702 of FIG. 7, data can be extracted for use by thetrimming system. The data can include patient teeth data (e.g., 3Dmodel/scanning of teeth) as well as clinical data about the patient(e.g., patient age, gender, prescription information, etc.). Next, atstep 704 of FIG. 7, the detection system can generate features from theextracted data, including raw tooth features and produced features, asdescribed above. Finally, at step 706 of FIG. 7, the features can bepassed into the classifier to determine a probability that the 3D toothmodel requires trimming and the location, tooth, or teeth within the 3Dtooth model that requires trimming.

The methods described herein may be performed by an apparatus, such as adata processing system, which may include hardware, software, and/orfirmware for performing many of these steps described above. Forexample, FIG. 8 is a simplified block diagram of a data processingsystem 500. Data processing system 500 typically includes at least oneprocessor 502 which communicates with a number of peripheral devicesover bus subsystem 504. These peripheral devices typically include astorage subsystem 506 (memory subsystem 508 and file storage subsystem514), a set of user interface input and output devices 518, and aninterface to outside networks 516, including the public switchedtelephone network. This interface is shown schematically as “Modems andNetwork Interface” block 516, and is coupled to corresponding interfacedevices in other data processing systems over communication networkinterface 524. Data processing system 500 may include a terminal or alow-end personal computer or a high-end personal computer, workstationor mainframe.

The user interface input devices typically include a keyboard and mayfurther include a pointing device and a scanner. The pointing device maybe an indirect pointing device such as a mouse, trackball, touchpad, orgraphics tablet, or a direct pointing device such as a touchscreenincorporated into the display. Other types of user interface inputdevices, such as voice recognition systems, may be used.

User interface output devices may include a printer and a displaysubsystem, which includes a display controller and a display devicecoupled to the controller. The display device may be a cathode ray tube(CRT), a flat-panel device such as a liquid crystal display (LCD), or aprojection device. The display subsystem may also provide nonvisualdisplay such as audio output.

Storage subsystem 506 maintains the basic programming and dataconstructs that provide the functionality of the present invention. Thesoftware modules discussed above are typically stored in storagesubsystem 506. Storage subsystem 506 typically comprises memorysubsystem 508 and file storage subsystem 514.

Memory subsystem 508 typically includes a number of memories including amain random access memory (RAM) 510 for storage of instructions and dataduring program execution and a read only memory (ROM) 512 in which fixedinstructions are stored. In the case of Macintosh-compatible personalcomputers the ROM would include portions of the operating system; in thecase of IBM-compatible personal computers, this would include the BIOS(basic input/output system).

File storage subsystem 514 provides persistent (nonvolatile) storage forprogram and data files, and typically includes at least one hard diskdrive and at least one floppy disk drive (with associated removablemedia). There may also be other devices such as a CD-ROM drive andoptical drives (all with their associated removable media).Additionally, the system may include drives of the type with removablemedia cartridges. The removable media cartridges may, for example behard disk cartridges, such as those marketed by Syquest and others, andflexible disk cartridges, such as those marketed by Iomega. One or moreof the drives may be located at a remote location, such as in a serveron a local area network or at a site on the Internet's World Wide Web.

In this context, the term “bus subsystem” is used generically so as toinclude any mechanism for letting the various components and subsystemscommunicate with each other as intended. With the exception of the inputdevices and the display, the other components need not be at the samephysical location. Thus, for example, portions of the file storagesystem could be connected over various local-area or wide-area networkmedia, including telephone lines. Similarly, the input devices anddisplay need not be at the same location as the processor, although itis anticipated that the present invention will most often be implementedin the context of PCS and workstations.

Bus subsystem 504 is shown schematically as a single bus, but a typicalsystem has a number of buses such as a local bus and one or moreexpansion buses (e.g., ADB, SCSI, ISA, EISA, MCA, NuBus, or PCI), aswell as serial and parallel ports. Network connections are usuallyestablished through a device such as a network adapter on one of theseexpansion buses or a modem on a serial port. The client computer may bea desktop system or a portable system.

Scanner 520 is responsible for scanning casts of the patient's teethobtained either from the patient or from an orthodontist and providingthe scanned digital data set information to data processing system 500for further processing. In a distributed environment, scanner 520 may belocated at a remote location and communicate scanned digital data setinformation to data processing system 500 over network interface 524.

Fabrication machine 522 fabricates dental appliances based onintermediate and final data set information acquired from dataprocessing system 500. In a distributed environment, fabrication machine522 may be located at a remote location and acquire data set informationfrom data processing system 500 over network interface 524.

Various alternatives, modifications, and equivalents may be used in lieuof the above components. Although the final position of the teeth may bedetermined using computer-aided techniques, a user may move the teethinto their final positions by independently manipulating one or moreteeth while satisfying the constraints of the prescription.

Additionally, the techniques described here may be implemented inhardware or software, or a combination of the two. The techniques may beimplemented in computer programs executing on programmable computersthat each includes a processor, a storage medium readable by theprocessor (including volatile and nonvolatile memory and/or storageelements), and suitable input and output devices. Program code isapplied to data entered using an input device to perform the functionsdescribed and to generate output information. The output information isapplied to one or more output devices.

Each program can be implemented in a high level procedural orobject-oriented programming language to operate in conjunction with acomputer system. However, the programs can be implemented in assembly ormachine language, if desired. In any case, the language may be acompiled or interpreted language.

Each such computer program can be stored on a storage medium or device(e.g., CD-ROM, hard disk or magnetic diskette) that is readable by ageneral or special purpose programmable computer for configuring andoperating the computer when the storage medium or device is read by thecomputer to perform the procedures described. The system also may beimplemented as a computer-readable storage medium, configured with acomputer program, where the storage medium so configured causes acomputer to operate in a specific and predefined manner.

Thus, any of the methods (including user interfaces) described hereinmay be implemented as software, hardware or firmware, and may bedescribed as a non-transitory computer-readable storage medium storing aset of instructions capable of being executed by a processor (e.g.,computer, tablet, smartphone, etc.), that when executed by the processorcauses the processor to control perform any of the steps, including butnot limited to: displaying, communicating with the user, analyzing,modifying parameters (including timing, frequency, intensity, etc.),determining, alerting, or the like.

While preferred embodiments of the present disclosure have been shownand described herein, it will be obvious to those skilled in the artthat such embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. Numerous differentcombinations of embodiments described herein are possible, and suchcombinations are considered part of the present disclosure. In addition,all features discussed in connection with any one embodiment herein canbe readily adapted for use in other embodiments herein. It is intendedthat the following claims define the scope of the invention and thatmethods and structures within the scope of these claims and theirequivalents be covered thereby.

When a feature or element is herein referred to as being “on” anotherfeature or element, it can be directly on the other feature or elementor intervening features and/or elements may also be present. Incontrast, when a feature or element is referred to as being “directlyon” another feature or element, there are no intervening features orelements present. It will also be understood that, when a feature orelement is referred to as being “connected”, “attached” or “coupled” toanother feature or element, it can be directly connected, attached orcoupled to the other feature or element or intervening features orelements may be present. In contrast, when a feature or element isreferred to as being “directly connected”, “directly attached” or“directly coupled” to another feature or element, there are nointervening features or elements present. Although described or shownwith respect to one embodiment, the features and elements so describedor shown can apply to other embodiments. It will also be appreciated bythose of skill in the art that references to a structure or feature thatis disposed “adjacent” another feature may have portions that overlap orunderlie the adjacent feature.

Terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention.For example, as used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, steps, operations, elements, components, and/orgroups thereof. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items and may beabbreviated as “/”.

Spatially relative terms, such as “under”, “below”, “lower”, “over”,“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of over and under. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly”, “downwardly”, “vertical”, “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

Although the terms “first” and “second” may be used herein to describevarious features/elements (including steps), these features/elementsshould not be limited by these terms, unless the context indicatesotherwise. These terms may be used to distinguish one feature/elementfrom another feature/element. Thus, a first feature/element discussedbelow could be termed a second feature/element, and similarly, a secondfeature/element discussed below could be termed a first feature/elementwithout departing from the teachings of the present invention.

Throughout this specification and the claims which follow, unless thecontext requires otherwise, the word “comprise”, and variations such as“comprises” and “comprising” means various components can be co-jointlyemployed in the methods and articles (e.g., compositions and apparatusesincluding device and methods). For example, the term “comprising” willbe understood to imply the inclusion of any stated elements or steps butnot the exclusion of any other elements or steps.

In general, any of the apparatuses and/or methods described hereinshould be understood to be inclusive, but all or a sub-set of thecomponents and/or steps may alternatively be exclusive, and may beexpressed as “consisting of” or alternatively “consisting essentiallyof” the various components, steps, sub-components or sub-steps.

As used herein in the specification and claims, including as used in theexamples and unless otherwise expressly specified, all numbers may beread as if prefaced by the word “about” or “approximately,” even if theterm does not expressly appear. The phrase “about” or “approximately”may be used when describing magnitude and/or position to indicate thatthe value and/or position described is within a reasonable expectedrange of values and/or positions. For example, a numeric value may havea value that is +/−0.1% of the stated value (or range of values), +/−1%of the stated value (or range of values), +/−2% of the stated value (orrange of values), +/−5% of the stated value (or range of values), +/−10%of the stated value (or range of values), etc. Any numerical valuesgiven herein should also be understood to include about or approximatelythat value, unless the context indicates otherwise. For example, if thevalue “10” is disclosed, then “about 10” is also disclosed. Anynumerical range recited herein is intended to include all sub-rangessubsumed therein. It is also understood that when a value is disclosedthat “less than or equal to” the value, “greater than or equal to thevalue” and possible ranges between values are also disclosed, asappropriately understood by the skilled artisan. For example, if thevalue “X” is disclosed the “less than or equal to X” as well as “greaterthan or equal to X” (e.g., where X is a numerical value) is alsodisclosed. It is also understood that the throughout the application,data is provided in a number of different formats, and that this data,represents endpoints and starting points, and ranges for any combinationof the data points. For example, if a particular data point “10” and aparticular data point “15” are disclosed, it is understood that greaterthan, greater than or equal to, less than, less than or equal to, andequal to 10 and 15 are considered disclosed as well as between 10 and15. It is also understood that each unit between two particular unitsare also disclosed. For example, if 10 and 15 are disclosed, then 11,12, 13, and 14 are also disclosed.

Although various illustrative embodiments are described above, any of anumber of changes may be made to various embodiments without departingfrom the scope of the invention as described by the claims. For example,the order in which various described method steps are performed mayoften be changed in alternative embodiments, and in other alternativeembodiments one or more method steps may be skipped altogether. Optionalfeatures of various device and system embodiments may be included insome embodiments and not in others. Therefore, the foregoing descriptionis provided primarily for exemplary purposes and should not beinterpreted to limit the scope of the invention as it is set forth inthe claims.

The examples and illustrations included herein show, by way ofillustration and not of limitation, specific embodiments in which thepatient matter may be practiced. As mentioned, other embodiments may beutilized and derived there from, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. Such embodiments of the inventive patient matter maybe referred to herein individually or collectively by the term“invention” merely for convenience and without intending to voluntarilylimit the scope of this application to any single invention or inventiveconcept, if more than one is, in fact, disclosed. Thus, althoughspecific embodiments have been illustrated and described herein, anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

1. A method comprising: acquiring a three-dimensional (3D) model of apatient's teeth; extracting one or more dental features of the patient'steeth from the 3D model of the patient's teeth, the one or more dentalfeatures corresponding to geometrical properties of the patient's teeth;applying the one or more dental features to a classifier the classifierconfigured to provide a probability the dental features correspond toone or more trimming factors forming a basis to trim at least a portionof the 3D model of the patient's teeth; and outputting from thecomputing device the probability the dental features correspond to theone or more trimming factors.
 2. The method of claim 1, furthercomprising trimming the 3D model of the patient's teeth based on theprobability the dental features correspond to the one or more trimmingfactors.
 3. The method of claim 1, further comprising trimming the 3Dmodel of the patient's teeth based on the probability the dentalfeatures correspond to the one or more trimming factors, wherein thetrimming step comprises trimming or removing at least one-third (⅓) of atarget tooth from the 3D model of the patient's teeth.
 4. The method ofclaim 1, wherein the probability the dental features correspond to theone or more trimming factors corresponds to a location within the 3Dmodel where trimming is desirable.
 5. The method of claim 1, furthercomprising creating the one or more dental features using one or moreraw features obtained from a scan of the patient's teeth.
 6. The methodof claim 1, further comprising: obtaining a scan of the patient's teeth;creating the one or more dental features using one or more raw featuresby performing a principal component analysis (PCA) of the raw features.7. The method of claim 1, further comprising taking the 3D model of thepatient's teeth.
 8. The method of claim 1, further comprising acquiringthe 3D model of the patient's teeth based on from an intraoral scanner.9. The method of claim 1, further comprising acquiring the 3D model ofthe patient's teeth based on a mold of the patient's teeth.
 10. Themethod of claim 1, wherein the classifier implements one or moreconvolutional neural networks (CNNs) configured to classify the dentalfeatures.
 11. A non-transitory computing device readable medium havinginstructions stored thereon, wherein the instructions are executable bya processor to cause a computing device to perform a method comprising:acquire a three-dimensional (3D) model of a patient's teeth; extract oneor more dental features of the patient's teeth from the 3D model of thepatient's teeth, the one or more dental features corresponding togeometrical properties of the patient's teeth; apply the one or moredental features to a classifier, the classifier configured to provide aprobability the dental features correspond to one or more trimmingfactors forming a basis to trim at least a portion of the 3D model ofthe patient's teeth; and output the probability the dental featurescorrespond to the one or more trimming factors.
 12. The non-transitorycomputing device readable medium of claim 11, wherein the methodcomprises trimming the 3D model of the patient's teeth based on theprobability the dental features correspond to the one or more trimmingfactors.
 13. The non-transitory computing device readable medium ofclaim 11, wherein the method further comprises trimming the 3D model ofthe patient's teeth based on the probability the dental featurescorrespond to the one or more trimming factors, wherein the trimmingcomprises trimming at least one-third (⅓) of a target tooth from the 3Dmodel of the patient's teeth.
 14. The non-transitory computing devicereadable medium of claim 11, wherein the probability the dental featurescorrespond to the one or more trimming factors corresponds to a locationwithin the 3D model where trimming is desirable.
 15. The non-transitorycomputing device readable medium of claim 11, wherein the methodcomprises creating the one or more dental features using one or more rawfeatures obtained from a scan of the patient's teeth.
 16. Thenon-transitory computing device readable medium of claim 11, wherein themethod comprises: obtaining a scan of the patient's teeth; obtaining oneor more raw features from the scan; creating the one or more dentalfeatures by performing a principal component analysis (PCA) of the rawfeatures of the patient's teeth.
 17. The non-transitory computing devicereadable medium of claim 11, wherein the method comprises creatingengineered features by taking a principal component analysis (PCA) ofthe raw features of the patient's teeth and further comprises creatingthe engineered features by performing automated feature exploration ofneighboring teeth dependency and local/regional arch shape from the rawfeatures of the patient's teeth.
 18. The non-transitory computing devicereadable medium of claim 12, wherein the method further comprisesacquiring the 3D model from an intraoral scanner. 19.-28. (canceled)